




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
						import torch

						from transformers.generation import (
						    MaxLengthCriteria,
						    MaxNewTokensCriteria,
						    MaxTimeCriteria,
						    StoppingCriteriaList,
						    validate_stopping_criteria,
						)







@require_torch
class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      3
							SCREAMING_SNAKE_CASE_   : str					  =      250

							SCREAMING_SNAKE_CASE_   : Tuple					  =      ids_tensor((batch_size, length)      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : Any					  =      torch.ones((batch_size, length)      ,device=snake_case__      ,dtype=torch.float ) / length
							return input_ids, scores






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[Any]					  =      self._get_tensors(5 )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10 ),
							        MaxTimeCriteria(max_time=0.1 ),
							    ] )

							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self._get_tensors(9 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      self._get_tensors(10 )
							self.assertTrue(criteria(snake_case__      ,snake_case__ ) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      MaxLengthCriteria(max_length=10 )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[Any]					  =      self._get_tensors(5 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : int					  =      self._get_tensors(9 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      self._get_tensors(10 )
							self.assertTrue(criteria(snake_case__      ,snake_case__ ) )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      MaxNewTokensCriteria(start_length=5      ,max_new_tokens=5 )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self._get_tensors(5 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self._get_tensors(9 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      self._get_tensors(10 )
							self.assertTrue(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      StoppingCriteriaList([criteria] )
							self.assertEqual(criteria_list.max_length      ,10 )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[Any]					  =      self._get_tensors(5 )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      MaxTimeCriteria(max_time=0.1 )
							self.assertFalse(criteria(snake_case__      ,snake_case__ ) )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      MaxTimeCriteria(max_time=0.1      ,initial_timestamp=time.time() - 0.2 )
							self.assertTrue(criteria(snake_case__      ,snake_case__ ) )






				def 		snake_case	(			self ):
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] )      ,10 )

							with self.assertWarns(snake_case__ ):
										validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] )      ,11 )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      validate_stopping_criteria(StoppingCriteriaList()      ,11 )

							self.assertEqual(len(snake_case__ )      ,1 )









"""simple docstring"""




from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase				:     Union[str, Any]					=				logging.get_logger(__name__)

lowerCAmelCase				:     Union[str, Any]					=				{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"mctct"





				def __init__(			self						,					_a=8_065						,					_a=1_536						,					_a=36						,					_a=6_144						,					_a=4						,					_a=384						,					_a=920						,					_a=1e-5						,					_a=0.3						,					_a="relu"						,					_a=0.02						,					_a=0.3						,					_a=0.3						,					_a=1						,					_a=0						,					_a=2						,					_a=1						,					_a=0.3						,					_a=1						,					_a=(7,)						,					_a=(3,)						,					_a=80						,					_a=1						,					_a=None						,					_a="sum"						,					_a=False						,					**_a						,					):





							"""simple docstring"""
							super().__init__(**_a						,					pad_token_id=_a						,					bos_token_id=_a						,					eos_token_id=_a					)
							lowerCamelCase      =   vocab_size
							lowerCamelCase      =   hidden_size
							lowerCamelCase      =   num_hidden_layers
							lowerCamelCase      =   intermediate_size
							lowerCamelCase      =   num_attention_heads
							lowerCamelCase      =   attention_head_dim
							lowerCamelCase      =   max_position_embeddings
							lowerCamelCase      =   layer_norm_eps
							lowerCamelCase      =   layerdrop
							lowerCamelCase      =   hidden_act
							lowerCamelCase      =   initializer_range
							lowerCamelCase      =   hidden_dropout_prob
							lowerCamelCase      =   attention_probs_dropout_prob
							lowerCamelCase      =   pad_token_id
							lowerCamelCase      =   bos_token_id
							lowerCamelCase      =   eos_token_id
							lowerCamelCase      =   conv_glu_dim
							lowerCamelCase      =   conv_dropout
							lowerCamelCase      =   num_conv_layers
							lowerCamelCase      =   input_feat_per_channel
							lowerCamelCase      =   input_channels
							lowerCamelCase      =   conv_channels
							lowerCamelCase      =   ctc_loss_reduction
							lowerCamelCase      =   ctc_zero_infinity

							# prevents config testing fail with exporting to json
							lowerCamelCase      =   list(_a					)
							lowerCamelCase      =   list(_a					)

							if len(self.conv_kernel					) != self.num_conv_layers:
										raise ValueError(
										    """Configuration for convolutional module is incorrect. """
										    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
										    f'but is `len(config.conv_kernel) = {len(self.conv_kernel					)}`, '
										    f'`config.num_conv_layers = {self.num_conv_layers}`.'					)



